Some years ago, an regularization approach known as Dropout was proposed by Srivastava et al. [1]} and aimed to turn off learning neurons using a random Bernoulli distribution. In other words, neurons and their outgoing and incoming connections are temporarily removed from the network according to a probability, allowing the evaluation of distinct sub-architectures and providing more robust training knowledge. Although it seems a straightforward method, the problem lies in that neurons are randomly dropped based only on a probability value (\(p\) ), not taking advantage of valuable information related to the model itself. Also, the \(p\)  value have to be carefully chosen, since high probabilities of shutting off neurons may negatively impact the learning process.
